Identifying influential nodes in social networks: Centripetal centrality and seed exclusion approach

Wang, Yan; Li, Haozhan; Zhang, Ling; Zhao, Linlin; Li, Wanlan

doi:10.1016/j.chaos.2022.112513

Cited by 29 publications

(5 citation statements)

References 50 publications

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“…New hybrid centrality measure: Temporal Closeness-Closeness measure. [21] Identification of influential spreaders.…”

Section: Studymentioning

confidence: 99%

“…However, the authors do not explore a broader range of centrality measures or compare this measure with other existing centrality measures comprehensively. The research conducted by the authors of [21] delves into online information propagation within complex networks, emphasizing the critical role of influential nodes in network structure and operation. The paper classifies centrality measures into global, local, and semi-local types, exploring their effectiveness in identifying influential nodes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Data Transfer by Evaluating Closeness Centrality for Dynamic Social Complex Network-Inspired Routing

López-Rourich,

Rodríguez-Pérez

2023

Applied Sciences

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Social Complex Networks in communication networks are pivotal for comprehending the impact of human-like interactions on information flow and communication efficiency. These networks replicate social behavior patterns in the digital realm by modeling device interactions, considering friendship, influence, and information-sharing frequency. A key challenge in communication networks is their dynamic topologies, driven by dynamic user behaviors, fluctuating traffic patterns, and scalability needs. Analyzing these changes is essential for optimizing routing and enhancing the user experience. This paper introduces a network model tailored for Opportunistic Networks, characterized by intermittent device connections and disconnections, resulting in sporadic connectivity. The model analyzes node behavior, extracts vital properties, and ranks nodes by influence. Furthermore, it explores the evolution of node connections over time, gaining insights into changing roles and their impact on data exchange. Real-world datasets validate the model’s effectiveness. Applying it enables the development of refined routing protocols based on dynamic influence rankings. This approach fosters more efficient, adaptive communication systems that dynamically respond to evolving network conditions and user behaviors.

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“…New hybrid centrality measure: Temporal Closeness-Closeness measure. [21] Identification of influential spreaders.…”

Section: Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Data Transfer by Evaluating Closeness Centrality for Dynamic Social Complex Network-Inspired Routing

López-Rourich,

Rodríguez-Pérez

2023

Applied Sciences

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“…Recall measures the completeness of the results measured by the algorithm [44]. Taking the UWUSRank as an example, the recall rate of user influence mining under Weibo topics is defined as shown in Formula (16).…”

Section: Rationalization Of User Influence Rankingmentioning

confidence: 99%

User Real-Time Influence Ranking Algorithm of Social Networks Considering Interactivity and Topicality

Piao

Sun

et al. 2023

Entropy

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At present, the existing influence evaluation algorithms often ignore network structure attributes, user interests and the time-varying propagation characteristics of influence. To address these issues, this work comprehensively discusses users’ own influence, weighted indicators, users’ interaction influence and the similarity between user interests and topics, thus proposing a dynamic user influence ranking algorithm called UWUSRank. First, we determine the user’s own basic influence based on their activity, authentication information and blog response. This improves the problem of poor objectivity of initial value on user influence evaluation when using PageRank to calculate user influence. Next, this paper mines users’ interaction influence by introducing the propagation network properties of Weibo (a Twitter-like service in China) information and scientifically quantifies the contribution value of followers’ influence to the users they follow according to different interaction influences, thereby solving the drawback of equal value transfer of followers’ influence. Additionally, we analyze the relevance of users’ personalized interest preferences and topic content and realize real-time monitoring of users’ influence at various time periods during the process of public opinion dissemination. Finally, we conduct experiments by extracting real Weibo topic data to verify the effectiveness of introducing each attribute of users’ own influence, interaction timeliness and interest similarity. Compared to TwitterRank, PageRank and FansRank, the results show that the UWUSRank algorithm improves the rationality of user ranking by 9.3%, 14.2%, and 16.7%, respectively, which proves the practicality of the UWUSRank algorithm. This approach can serve as a guide for research on user mining, information transmission methods, and public opinion tracking in social network-related areas.

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“…Simple greedy strategies can provide solutions having ( true0 1 − 1 / normale )-approximation guarantee [10,22], but their time complexity is still too high to handle large-scale networks. Some heuristic algorithms [23,24] are more efficient without theoretical guarantees using centrality strategies or assuming that the underlying diffusion path is the shortest path. The reverse influence sampling method, which uses reverse reachability to acquire substantial samples, greatly improves the efficiency of identifying the most influential nodes [25].…”

Section: Introductionmentioning

confidence: 99%

Individual-centralized seeding strategy for influence maximization in information-limited networks

Liu,

Wang,

Wang

et al. 2024

J. R. Soc. Interface.

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Peer effects can directly or indirectly rely on interaction networks to drive people to follow ideas or behaviours triggered by a few individuals, and such effects can be largely improved by targeting the so-called influential individuals. In this article, we study the current most promising seeding strategy used in field experiments, the one-hop strategy, where the underlying interaction networks are generally too impractical or prohibitively expensive to be obtained, and propose an individual-centralized seeding approach to target influential seeds in information-limited networks. The presented strategy works by reasonable follow-up questions to respondents, such as Who do you think has more connections/friends? , and constructs the seeding set by those nodes with the most nominations. In this manner, the proposed method could acquire more information about the studied interaction network from the inference of respondents without surveying additional individuals. We evaluate our strategy on networks from various experimental datasets. Results show that the obtained seeds are much more influential compared to the one-hop strategy and other methods. We also show how the proposed approach could be implemented in field studies and potentially provide better interventions in real scenarios.

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Identifying influential nodes in social networks: Centripetal centrality and seed exclusion approach

Cited by 29 publications

References 50 publications

Efficient Data Transfer by Evaluating Closeness Centrality for Dynamic Social Complex Network-Inspired Routing

Efficient Data Transfer by Evaluating Closeness Centrality for Dynamic Social Complex Network-Inspired Routing

User Real-Time Influence Ranking Algorithm of Social Networks Considering Interactivity and Topicality

Individual-centralized seeding strategy for influence maximization in information-limited networks

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